A star identification algorithm based on radial and dynamic cyclic features of star pattern

The reliability of star sensor in a harsh environment has recently become a research hotspot. In some harsh environment such as plume interference, a large number of false stars can be observed, leading to failure in star identification. In this paper, we propose a false star filtering algorithm, which can be used as a preprocessing algorithm for any existing star identification algorithm. By utilizing the difference between the motion of false stars and true stars, the algorithm performs angular distance tracking and star voting on multiple consecutive frames of star images and achieves false star filtering. The software simulation results show that for the star images containing more than 700 false stars, the algorithm is able to find out all true stars in less than 10 frames, and the success rate of the algorithm remains high when the star sensor rotates at up to 1°/s. The algorithm is also implemented on an existing star senor and evaluated with star images generated by a dynamic star simulator. The experimental results indicate that with the help of the proposed algorithm, the robustness of a normal star identification algorithm can be significantly improved.

Taking a large number of images, the Cassini Imaging Science Subsystem(ISS) has been routinely used in astrometry. In ISS images, disk-resolved objects often lead to false detection of stars that disturb the camera pointing correction. The aimof this study was to develop an automated processingmethod to remove the false image stars in disk-resolved objects in ISS images. The method included the following steps: extracting edges, segmenting boundary arcs, fitting circles and excluding false image stars. The proposed method was tested using 200 ISS images. Preliminary experimental results show that it can remove the false image stars in more than 95% of ISS images with disk-resolved objects in a fully automatic manner, i.e., outperforming the traditional circle detection based on Circular Hough Transform (CHT) by 17%. In addition, its speed is more than twice as fast as that of the CHT method. It is also more robust (no manual parameter tuning is needed) when compared with CHT. The proposed method was also applied to a set of ISS images of Rhea to eliminate the mismatch in pointing correction in automatic procedure. Experiment results showed that the precision of final astrometry results can be improve by roughly 2 times that of automatic procedure without the method. It proved that the proposed method is helpful in the astrometry of ISS images in a fully automatic manner.

The authors of this paper propose a robust star identification algorithm for a ‘Lost-In-Space’-mode star tracker for lost-cost CubeSat missions. A two-step identification framework and an embedded validation mechanism were designed to accelerate the process. In the first step, a masked distance map is designed to provide a shortlist of stars, and the embedded fast validation process enables the direct output of validated stars before the second step. In the second step, local similarity is utilized to select a set of stars from those shortlisted, and the final validation procedure rejects all unsatisfactory stars. This algorithm can provide reliable and robust recognition even when the captured star images include severe star positioning errors, missing stars and false stars. The proposed algorithm was verified by a simulation study under various conditions. As low-cost star sensors face harsh and unknown environments during deep space CubeSat missions such as asteroid exploration, the proposed algorithm with high robustness will provide an important function.

A full-sky autonomous star identification is presented. Star pattern is generated in the form of radial and cyclic features. Since radial pattern is a reliable feature, it is used for initial match, and cyclic pattern is then used in re-match step. Simulation is done to verify the performance and the result shows this method is more robust against location error compared with grid algorithm under the same condition.

As one of the best image clustering methods, fuzzy local information C-means is often used for image segmentation. The effects of noise are avoided by utilizing the spatial relationship among pixels, but it often generates boundary zones for the mix pixels around the edges. This letter presents an image spatial clustering method, called fuzzy C-means with edge and local information (FELICM), which reduces the edge degradation by introducing the weights of pixels within local neighbor windows. The edges are extracted at first by Canny edge detection. During detection, two adaptive thresholds obtained by multi-Otsu method are used. Then, different weights are set according to whether the window center and the local neighbors are separated by an edge or not. Pixels, together with different weighted local neighbors, are clustered iteratively, until the final clustering result is obtained. The method can be directly applied to the image without any filter preprocessing, and the experimental results over remote sensing images show that FELICM not only effectively solves the problem of isolated and random distribution of pixels inside regions but also obtains high edge accuracies.

Similarities and differences in dynamic properties of brain networks between internet gaming disorder and tobacco use disorder

In this paper, we studied on an attenuation effect of automobile exhaust noise according to the direction of canceling speaker in ANC system. Automobile exhaust noise was recorded at 800 rpm, 3500 rpm and 5000 rpm of a diesel engine. Directions of canceling speaker can be set to <TEX>$30^{\circ}$</TEX>, <TEX>$90^{\circ}$</TEX> and <TEX>$150^{\circ}$</TEX> against the primary noise flow by acrylic ducts to be made for the experimentation. DSP board with TMS320C6416 chip of Texas Instrument Co. used to control the ANC system. The algorithm of this ANC system applied the Filtered-x-LMS algorithm that is modified to compensate for a property of DSP input signal and the secondary-path effect. As an experiment result, the direction of canceling speaker was proved to influence the reduction effect of noise. The <TEX>$150^{\circ}$</TEX> duct in the attenuation effect of noise showed a better result than the <TEX>$90^{\circ}$</TEX> or <TEX>$30^{\circ}$</TEX> duct.

In this paper, we present an approach to comparing control flow graphs of binary programs by matching their basic blocks. We first set up an initial match and propagate it to reach a stable state. We consider the matched pairs to identify overall similarities. To evaluate the proposed method, we perform experiments on real-world Java applications, and compare their performance with previous structural matching method. In the experimental results, the proposed method shows more reliable results than previous method at distinguishing similar control flow graphs.

Assuming that neighboring pixels with similar colors in stereo images, share almost the same disparities, this paper presents a new global stereo matching algorithm based on Belief Propagation and cross-based aggregation method. In this approach the hierarchical Belief Propagation strategy is followed by a left-right consistency check as an initial match. Then a refinement algorithm is applied to generated disparity map, based on cross-based region method. The initial matching performed by hierarchical Belief Propagation strategy uses less memory and improves the running speed of energy minimization function. The experimental results, evaluated by the Middlebury data sets, show that the proposed method improves the accuracy of disparity map compare to other real-time stereo matching algorithms.

An important challenge associated with wind power is the unpredictable nature of the wind. As wind power forecasting is usually based on the relationship between wind speeds and wind power, wind farm data is a critical prerequisite for wind speed prediction, meanwhile it plays an important role on the reliability and safety of the grids. For development of new controls, converter topologies, modulation methods, and maximum power extraction algorithms for wind energy conversion systems, the provision of a controlled test environment showing dynamic and steady state characteristics of wind turbines is desired. In this paper, a small scale wind turbine experimental laboratory model based on separately excited dc-shunt motor and permanent magnet synchronous generator (PMSG) is proposed, which reproduces the static and dynamic characteristics of the wind turbine without any wind environment. A programmable wind speed model is established that can satisfactorily simulate the spatial effect of the wind behavior, including gust change, ramp variations, and noise effect. The dynamics of the simulator is studied using the drive train model of the armature controlled dc shunt motor. A microcontroller along with high frequency buck converter has been used for acceleration and deceleration of the dc motor. It is shown through the frequency response characteristics that the simulator model approximates the wind turbine model dynamics. Simulation and experimental results illustrate that the speed of the motor changes with variation in the applied terminal voltage, like in the real wind turbine where the turbine speed changes according to the wind speed. Simulation results are obtained through PSCAD/EMTDC simulation software and experimental results are obtained through the programmable microcontroller used with the DC-motor-PMSG set.

Dual-granularity weighted ensemble clustering

The application of a new algorithm, known as genetic regression (GR), to calibration problems with spectra containing complex fluctuating baselines is illustrated with the use of synthetic data. The ability of the algorithm to automatically compensate for the presence of linear and polynomial (quadratic and cubic) baselines in the presence of complex spectral overlap is investigated along with the effect of noise. GR is unique in that it provides an effective wavelength optimization technique by sorting through the spectr al data and selecting and appropriately combining wavelengths that compensate for structured baseline and spectral overlap. The results obtained with GR are compared with those obtained with background-corrected linear regression. GR is shown to give much better results and, in constrast to traditional background correction, is much faster and can compensate for the presence of both structured baseline and complex spectral overlap simultaneously. The results of a noise study show that the method works at low signal-to-noise ratio (SNR) and that the error in the final result is a function of the noise.

The dynamic mechanical properties and crack evolution characteristics of coal and rock during split Hopkinson pressure bar (SHPB) impact failure are important contents for analysis. In previous studies, the coal and rock specimens used have usually been independent and not closely correlated. In addition, quantitative characterization and analysis methods for coal and rock cracks are immature, and more information has not been fully revealed. The aims of this paper are to comprehensively explore both the dynamic mechanical properties and crack evolution characteristics of coal and rock during impact failure. First, experimental specimens are prepared from coal seam, direct roof rock strata and direct floor rock strata in the same area to highlight the correlations between test pieces. Second, a dynamic strain gauge and high-speed (HS) camera are adopted to reflect the stress wave signal and crack evolution. Then, based on digital image correlation (DIC) technology and the mass screening method, the evolution laws of surface cracks during crushing and the distribution characteristics of sample fragments after crushing are studied from the perspective of fractal, and finally compared with those of the simulation analysis. The results are as follows. (1) The coal and rock samples from the same area have both consistency and differences. The dynamic mechanical properties of coal and rock are affected by the impact velocity and the physical properties of the specimen. Higher impact speeds and densities lead to the more obvious brittleness of the specimen when destroyed. Conversely, the sample shows more plasticity and ductile yield. (2) The self-similarity is significantly manifested in the evolution of surface cracks during impact and the distribution characteristics of fragments after impact. The box dimension and quality screening dimension are applicable to quantitatively characterize the evolution process and results of coal and rock fractures. (3) The simulation results based on the Holmquist–Johnson–Cook (HJC) and Riedel–Hiermaier–Thoma (RHT) constitutive models agree well with the experimental results, and the RHT constitutive model is more consistent. This study may contribute to a more comprehensive understanding of the dynamic characteristics and crack evolution laws of coal and rock under impact loading and provide references for further research and discussion.

Numerical and experimental investigation on dynamic performance of bump foil journal bearing based on journal orbit

Thermal-hydraulic modeling and simulation are heavily dependent on the knowledge of flow regimes, which sort multiphase flow patterns into classifications with static boundaries that display characteristic features of flow patterns and phase distributions. Flow regime maps heretofore have been primarily developed mainly for vertical or horizontal pipe orientations by capturing images of flows at varying conditions and assigning them to different flow regimes based on visualization studies. As such, this method is inherently subjective. Furthermore, classifying two-phase flows into regimes with static boundaries is not physical, as two-phase flows undergo gradual changes as they transition from one regime to another. Therefore, such a static approach is not only unphysical but also inaccurate, resulting in the erroneous analysis of two-phase flows. Additionally, subjective observations at such flow conditions are prone to disagreement and inconsistencies in labeling. These difficulties are exacerbated in inclined two-phase flows. In this study, a novel two-phase flow pattern map employing the dynamic red, green, and blue (RGB) color model is presented. The model contains the embedded information of three normalized time-averaged statistics from signals obtained by a dual-ring impedance meter. A database of impedance meter signals from a 25.4-mm-inner-diameter adiabatic air-water inclinable test facility is used to compute statistics for each experimental flow condition. Multiple statistical measures are evaluated, and a principal component analysis is performed for feature selection. By mapping each parameter to intensities of red, green, and blue, a dynamic RGB flow pattern map can be produced in which groups of similar colors are representative of various flow regimes and the dynamic transition characteristics between the flow regimes are characterized physically. Therefore, this new method makes viable the observation of how flow patterns gradually change with respect to angle, as well as the fuzzification or quantification of the uncertainty surrounding experimental transition boundaries.